{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Exploratory Data Analysis (EDA)\n",
    "\n",
    "<img src=\"http://www.codeheroku.com/static/blog/images/pid17_data.png\">\n",
    "\n",
    "<blockquote>“Torture the data, and it will confess to anything.”\n",
    "\n",
    "— Ronald Coase</blockquote>\n",
    "\n",
    "If you are someone who is familiar with data science, I can confidently say that you must have realized the power of the above statement.\n",
    "\n",
    "With proper utilization of data, even ruling the entire world is possible.\n",
    "\n",
    "But, having raw, unprocessed data is meaningless unless you analyze it to get significant insights.\n",
    "\n",
    "Exploratory Data Analysis (EDA) is the process of visualizing and analyzing data to extract significant insights from it. In other words, EDA is the process of summarizing main characteristics of data in order to gain better understanding of the dataset.\n",
    "\n",
    "In this article, we are going to introduce you to the process of EDA through the analysis of the automobile dataset available [here](https://drive.google.com/file/d/1g7ewVnbl3zHW33eOD9DoDwJPc4f-OPU2/view?usp=sharing). We will talk about some common methods used for EDA and will let you know how to apply them for extracting meaningful insights from raw data.\n",
    "\n",
    "Here is a quick overview of the things that you are going to learn in this article:\n",
    "\n",
    "- Descriptive Statistics\n",
    "- Grouping of Data\n",
    "- Handling missing values in dataset\n",
    "- ANOVA: Analysis of variance\n",
    "- Correlation\n",
    "\n",
    "At first, you need to download the [automobile dataset](https://drive.google.com/file/d/1g7ewVnbl3zHW33eOD9DoDwJPc4f-OPU2/view?usp=sharing) from [this link](https://drive.google.com/file/d/1g7ewVnbl3zHW33eOD9DoDwJPc4f-OPU2/view?usp=sharing).\n",
    "\n",
    "## Descriptive statistics:\n",
    "\n",
    "Descriptive statistics analysis helps to describe the basic features of dataset and obtain a brief summary of the data.\n",
    "\n",
    "The describe() method in Pandas library helps us to have a brief summary of the dataset. It automatically calculates basic statistics for all numerical variables excluding NaN (we will come to this part later) values.\n",
    "\n",
    "Let’s import all the libraries and read the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "df = pd.read_csv('automobile.csv') #read data from CSV file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check if the data is successfully imported by displaying the first 5 rows of dataframe using head() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make aspiration num-of-doors  \\\n",
       "0          3                122  alfa-romero        std          two   \n",
       "1          3                122  alfa-romero        std          two   \n",
       "2          1                122  alfa-romero        std          two   \n",
       "3          2                164         audi        std         four   \n",
       "4          2                164         audi        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n",
       "0  convertible          rwd           front        88.6  0.811148  ...   \n",
       "1  convertible          rwd           front        88.6  0.811148  ...   \n",
       "2    hatchback          rwd           front        94.5  0.822681  ...   \n",
       "3        sedan          fwd           front        99.8  0.848630  ...   \n",
       "4        sedan          4wd           front        99.4  0.848630  ...   \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n",
       "0                9.0       111.0    5000.0       21          27  13495.0   \n",
       "1                9.0       111.0    5000.0       21          27  16500.0   \n",
       "2                9.0       154.0    5000.0       19          26  16500.0   \n",
       "3               10.0       102.0    5500.0       24          30  13950.0   \n",
       "4                8.0       115.0    5500.0       18          22  17450.0   \n",
       "\n",
       "  city-L/100km  horsepower-binned  diesel  gas  \n",
       "0    11.190476             Medium       0    1  \n",
       "1    11.190476             Medium       0    1  \n",
       "2    12.368421             Medium       0    1  \n",
       "3     9.791667             Medium       0    1  \n",
       "4    13.055556             Medium       0    1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let’s apply the describe() method over this dataset and see the results. It displays a description of mean, standard deviation, quartiles and maximum & minimum values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.00000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.840796</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>98.797015</td>\n",
       "      <td>0.837102</td>\n",
       "      <td>0.915126</td>\n",
       "      <td>53.766667</td>\n",
       "      <td>2555.666667</td>\n",
       "      <td>126.875622</td>\n",
       "      <td>3.330692</td>\n",
       "      <td>3.256904</td>\n",
       "      <td>10.164279</td>\n",
       "      <td>103.405534</td>\n",
       "      <td>5117.665368</td>\n",
       "      <td>25.179104</td>\n",
       "      <td>30.686567</td>\n",
       "      <td>13207.129353</td>\n",
       "      <td>9.944145</td>\n",
       "      <td>0.099502</td>\n",
       "      <td>0.900498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.254802</td>\n",
       "      <td>31.99625</td>\n",
       "      <td>6.066366</td>\n",
       "      <td>0.059213</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>2.447822</td>\n",
       "      <td>517.296727</td>\n",
       "      <td>41.546834</td>\n",
       "      <td>0.268072</td>\n",
       "      <td>0.319256</td>\n",
       "      <td>4.004965</td>\n",
       "      <td>37.365700</td>\n",
       "      <td>478.113805</td>\n",
       "      <td>6.423220</td>\n",
       "      <td>6.815150</td>\n",
       "      <td>7947.066342</td>\n",
       "      <td>2.534599</td>\n",
       "      <td>0.300083</td>\n",
       "      <td>0.300083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>0.678039</td>\n",
       "      <td>0.837500</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "      <td>4.795918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.00000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>0.801538</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2169.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7775.000000</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.832292</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>137.00000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>0.881788</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2926.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.00000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "      <td>18.076923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  normalized-losses  wheel-base      length       width  \\\n",
       "count  201.000000          201.00000  201.000000  201.000000  201.000000   \n",
       "mean     0.840796          122.00000   98.797015    0.837102    0.915126   \n",
       "std      1.254802           31.99625    6.066366    0.059213    0.029187   \n",
       "min     -2.000000           65.00000   86.600000    0.678039    0.837500   \n",
       "25%      0.000000          101.00000   94.500000    0.801538    0.890278   \n",
       "50%      1.000000          122.00000   97.000000    0.832292    0.909722   \n",
       "75%      2.000000          137.00000  102.400000    0.881788    0.925000   \n",
       "max      3.000000          256.00000  120.900000    1.000000    1.000000   \n",
       "\n",
       "           height  curb-weight  engine-size        bore      stroke  \\\n",
       "count  201.000000   201.000000   201.000000  201.000000  197.000000   \n",
       "mean    53.766667  2555.666667   126.875622    3.330692    3.256904   \n",
       "std      2.447822   517.296727    41.546834    0.268072    0.319256   \n",
       "min     47.800000  1488.000000    61.000000    2.540000    2.070000   \n",
       "25%     52.000000  2169.000000    98.000000    3.150000    3.110000   \n",
       "50%     54.100000  2414.000000   120.000000    3.310000    3.290000   \n",
       "75%     55.500000  2926.000000   141.000000    3.580000    3.410000   \n",
       "max     59.800000  4066.000000   326.000000    3.940000    4.170000   \n",
       "\n",
       "       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\n",
       "count         201.000000  201.000000   201.000000  201.000000   201.000000   \n",
       "mean           10.164279  103.405534  5117.665368   25.179104    30.686567   \n",
       "std             4.004965   37.365700   478.113805    6.423220     6.815150   \n",
       "min             7.000000   48.000000  4150.000000   13.000000    16.000000   \n",
       "25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n",
       "50%             9.000000   95.000000  5125.369458   24.000000    30.000000   \n",
       "75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \n",
       "max            23.000000  262.000000  6600.000000   49.000000    54.000000   \n",
       "\n",
       "              price  city-L/100km      diesel         gas  \n",
       "count    201.000000    201.000000  201.000000  201.000000  \n",
       "mean   13207.129353      9.944145    0.099502    0.900498  \n",
       "std     7947.066342      2.534599    0.300083    0.300083  \n",
       "min     5118.000000      4.795918    0.000000    0.000000  \n",
       "25%     7775.000000      7.833333    0.000000    1.000000  \n",
       "50%    10295.000000      9.791667    0.000000    1.000000  \n",
       "75%    16500.000000     12.368421    0.000000    1.000000  \n",
       "max    45400.000000     18.076923    1.000000    1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It helps us to have a brief overview of the data. For example, by seeing the mean of “diesel” and “gas” column, we can say that 90% of the engines run on gas while almost 10% engines run on diesel.\n",
    "\n",
    "Now we know how how to get brief summary of numerical data using describe() method. But, what if we have categorical data? How can we get a summary of categorical data? The value_counts() method will be useful in this case.\n",
    "\n",
    "Let’s see an example of this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    115\n",
       "two      86\n",
       "Name: num-of-doors, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above output tells us that our dataset has 115 cars with four doors and 86 cars with two doors.\n",
    "\n",
    "### Box plots\n",
    "\n",
    "A better approach of visualizing numerical data would be to use box plots. Box plot shows us the median of the data, which represents where the middle data point is. The upper and lower quartiles represent the 75 and 25 percentile of the data respectively. The upper and lower extremes shows us the extreme ends of the distribution of our data. Finally, it also represents outliers, which occur outside the upper and lower extremes.\n",
    "\n",
    "<img src=\"http://www.codeheroku.com/static/blog/images/pid17_bxplt_desc.png\">\n",
    "\n",
    "For example, the following code shows us the distribution of price for cars with different number of cylinders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8481a9c588>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='num-of-cylinders',y='price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the price of eight-cylinder cars lies between 35000 to 45000, while the price of four-cylinder cars lies between 5000 to 19000. It also tells us that the average price of five-cylinder cars and six-cylinder cars is almost same.\n",
    "\n",
    "### Scatter plots\n",
    "\n",
    "Often, we see some continuous variables in our data within a specific range. For example, in our dataset, engine size and price are continuous variables. What if we want to understand the relationship between these continuous variables? Could engine size possibly predict the price of the car?\n",
    "\n",
    "A great way to visualize this relationship would be to use a scatter plot. Scatter plots represent each relationship between two continuous variables as individual data point in a 2D graph.\n",
    "\n",
    "We will use the scatter() method of matplotlib library to visualize the scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['engine-size'],df['price'])\n",
    "plt.xlabel('Engine Size')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above output, we can interpret that there is a linear relationship between engine size and price. Cars with bigger engines might be costlier than the cars with small-sized engines. This thing totally makes sense, right?\n",
    "\n",
    "### Histograms\n",
    "\n",
    "Histogram shows us the frequency distribution of a variable. It partitions the spread of numeric data into parts called as “bins” and then counts the number of data points that fall into each bin. So, the vertical axis actually represents the number of data points in each bin.\n",
    "\n",
    "Let’s see an example of this. We will see the distribution of “peak-rpm” using histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count,bin_edges = np.histogram(df['peak-rpm'])\n",
    "\n",
    "df['peak-rpm'].plot(kind='hist',xticks=bin_edges)\n",
    "\n",
    "plt.xlabel('Value of peak rpm')\n",
    "plt.ylabel('Number of cars')\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above output tells us that there are 10 cars which have peak rpm between 4395 and 4640, around 42 cars have peak rpm between 4640 and 4885 and so on.\n",
    "\n",
    "## Grouping of data\n",
    "\n",
    "Assume that you want to know the average price of different types of vehicles and observe how they differ according to body styles and number of doors. A nice way to do this would be to group the data according to “body-style” and “num-of-doors” and then see the average price across each category. The groupby() method from Pandas library helps us to accomplish this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>four</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>8372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>14490.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>four</td>\n",
       "      <td>wagon</td>\n",
       "      <td>12371.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>21890.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>22208.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>10230.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>14283.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  num-of-doors   body-style         price\n",
       "0         four    hatchback   8372.000000\n",
       "1         four        sedan  14490.687500\n",
       "2         four        wagon  12371.960000\n",
       "3          two  convertible  21890.500000\n",
       "4          two      hardtop  22208.500000\n",
       "5          two    hatchback  10230.793103\n",
       "6          two        sedan  14283.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = df[['num-of-doors','body-style','price']]\n",
    "\n",
    "df_group = df_temp.groupby(['num-of-doors','body-style'],as_index=False).mean()\n",
    "\n",
    "df_group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above output tells us that two door hardtop and two door convertibles are the most expensive cars, whereas, four door hatchbacks are the cheapest.\n",
    "\n",
    "A table of this form is not very easy to read. So, we can convert this table to a pivot table using the pivot() method, which would allow us to read this table in a better fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "      <th>hardtop</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22208.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hatchback</th>\n",
       "      <td>8372.0000</td>\n",
       "      <td>10230.793103</td>\n",
       "    </tr>\n",
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       "      <th>sedan</th>\n",
       "      <td>14490.6875</td>\n",
       "      <td>14283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>12371.9600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price              \n",
       "num-of-doors        four           two\n",
       "body-style                            \n",
       "convertible          NaN  21890.500000\n",
       "hardtop              NaN  22208.500000\n",
       "hatchback      8372.0000  10230.793103\n",
       "sedan         14490.6875  14283.000000\n",
       "wagon         12371.9600           NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = df_group.pivot(index='body-style',columns='num-of-doors')\n",
    "\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The price data now becomes a rectangular grid, which is easier to visualize.\n",
    "\n",
    "## Handling missing values\n",
    "\n",
    "When no data value is stored for a feature in a particular observation, we say this feature has missing values. Examining this is important because when some of your data is missing, it can lead to weak or biased analysis.\n",
    "\n",
    "We can detect missing values by applying isnull() method over the dataframe. The isnull() method returns a rectangular grid of boolean values which tells us if a particular cell in the dataframe has missing value or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  normalized-losses   make  aspiration  num-of-doors  \\\n",
       "0        False              False  False       False         False   \n",
       "1        False              False  False       False         False   \n",
       "2        False              False  False       False         False   \n",
       "3        False              False  False       False         False   \n",
       "4        False              False  False       False         False   \n",
       "5        False              False  False       False         False   \n",
       "6        False              False  False       False         False   \n",
       "7        False              False  False       False         False   \n",
       "8        False              False  False       False         False   \n",
       "9        False              False  False       False         False   \n",
       "10       False              False  False       False         False   \n",
       "11       False              False  False       False         False   \n",
       "12       False              False  False       False         False   \n",
       "13       False              False  False       False         False   \n",
       "14       False              False  False       False         False   \n",
       "15       False              False  False       False         False   \n",
       "16       False              False  False       False         False   \n",
       "17       False              False  False       False         False   \n",
       "18       False              False  False       False         False   \n",
       "19       False              False  False       False         False   \n",
       "20       False              False  False       False         False   \n",
       "21       False              False  False       False         False   \n",
       "22       False              False  False       False         False   \n",
       "23       False              False  False       False         False   \n",
       "24       False              False  False       False         False   \n",
       "25       False              False  False       False         False   \n",
       "26       False              False  False       False         False   \n",
       "27       False              False  False       False         False   \n",
       "28       False              False  False       False         False   \n",
       "29       False              False  False       False         False   \n",
       "..         ...                ...    ...         ...           ...   \n",
       "171      False              False  False       False         False   \n",
       "172      False              False  False       False         False   \n",
       "173      False              False  False       False         False   \n",
       "174      False              False  False       False         False   \n",
       "175      False              False  False       False         False   \n",
       "176      False              False  False       False         False   \n",
       "177      False              False  False       False         False   \n",
       "178      False              False  False       False         False   \n",
       "179      False              False  False       False         False   \n",
       "180      False              False  False       False         False   \n",
       "181      False              False  False       False         False   \n",
       "182      False              False  False       False         False   \n",
       "183      False              False  False       False         False   \n",
       "184      False              False  False       False         False   \n",
       "185      False              False  False       False         False   \n",
       "186      False              False  False       False         False   \n",
       "187      False              False  False       False         False   \n",
       "188      False              False  False       False         False   \n",
       "189      False              False  False       False         False   \n",
       "190      False              False  False       False         False   \n",
       "191      False              False  False       False         False   \n",
       "192      False              False  False       False         False   \n",
       "193      False              False  False       False         False   \n",
       "194      False              False  False       False         False   \n",
       "195      False              False  False       False         False   \n",
       "196      False              False  False       False         False   \n",
       "197      False              False  False       False         False   \n",
       "198      False              False  False       False         False   \n",
       "199      False              False  False       False         False   \n",
       "200      False              False  False       False         False   \n",
       "\n",
       "     body-style  drive-wheels  engine-location  wheel-base  length  ...  \\\n",
       "0         False         False            False       False   False  ...   \n",
       "1         False         False            False       False   False  ...   \n",
       "2         False         False            False       False   False  ...   \n",
       "3         False         False            False       False   False  ...   \n",
       "4         False         False            False       False   False  ...   \n",
       "5         False         False            False       False   False  ...   \n",
       "6         False         False            False       False   False  ...   \n",
       "7         False         False            False       False   False  ...   \n",
       "8         False         False            False       False   False  ...   \n",
       "9         False         False            False       False   False  ...   \n",
       "10        False         False            False       False   False  ...   \n",
       "11        False         False            False       False   False  ...   \n",
       "12        False         False            False       False   False  ...   \n",
       "13        False         False            False       False   False  ...   \n",
       "14        False         False            False       False   False  ...   \n",
       "15        False         False            False       False   False  ...   \n",
       "16        False         False            False       False   False  ...   \n",
       "17        False         False            False       False   False  ...   \n",
       "18        False         False            False       False   False  ...   \n",
       "19        False         False            False       False   False  ...   \n",
       "20        False         False            False       False   False  ...   \n",
       "21        False         False            False       False   False  ...   \n",
       "22        False         False            False       False   False  ...   \n",
       "23        False         False            False       False   False  ...   \n",
       "24        False         False            False       False   False  ...   \n",
       "25        False         False            False       False   False  ...   \n",
       "26        False         False            False       False   False  ...   \n",
       "27        False         False            False       False   False  ...   \n",
       "28        False         False            False       False   False  ...   \n",
       "29        False         False            False       False   False  ...   \n",
       "..          ...           ...              ...         ...     ...  ...   \n",
       "171       False         False            False       False   False  ...   \n",
       "172       False         False            False       False   False  ...   \n",
       "173       False         False            False       False   False  ...   \n",
       "174       False         False            False       False   False  ...   \n",
       "175       False         False            False       False   False  ...   \n",
       "176       False         False            False       False   False  ...   \n",
       "177       False         False            False       False   False  ...   \n",
       "178       False         False            False       False   False  ...   \n",
       "179       False         False            False       False   False  ...   \n",
       "180       False         False            False       False   False  ...   \n",
       "181       False         False            False       False   False  ...   \n",
       "182       False         False            False       False   False  ...   \n",
       "183       False         False            False       False   False  ...   \n",
       "184       False         False            False       False   False  ...   \n",
       "185       False         False            False       False   False  ...   \n",
       "186       False         False            False       False   False  ...   \n",
       "187       False         False            False       False   False  ...   \n",
       "188       False         False            False       False   False  ...   \n",
       "189       False         False            False       False   False  ...   \n",
       "190       False         False            False       False   False  ...   \n",
       "191       False         False            False       False   False  ...   \n",
       "192       False         False            False       False   False  ...   \n",
       "193       False         False            False       False   False  ...   \n",
       "194       False         False            False       False   False  ...   \n",
       "195       False         False            False       False   False  ...   \n",
       "196       False         False            False       False   False  ...   \n",
       "197       False         False            False       False   False  ...   \n",
       "198       False         False            False       False   False  ...   \n",
       "199       False         False            False       False   False  ...   \n",
       "200       False         False            False       False   False  ...   \n",
       "\n",
       "     compression-ratio  horsepower  peak-rpm  city-mpg  highway-mpg  price  \\\n",
       "0                False       False     False     False        False  False   \n",
       "1                False       False     False     False        False  False   \n",
       "2                False       False     False     False        False  False   \n",
       "3                False       False     False     False        False  False   \n",
       "4                False       False     False     False        False  False   \n",
       "5                False       False     False     False        False  False   \n",
       "6                False       False     False     False        False  False   \n",
       "7                False       False     False     False        False  False   \n",
       "8                False       False     False     False        False  False   \n",
       "9                False       False     False     False        False  False   \n",
       "10               False       False     False     False        False  False   \n",
       "11               False       False     False     False        False  False   \n",
       "12               False       False     False     False        False  False   \n",
       "13               False       False     False     False        False  False   \n",
       "14               False       False     False     False        False  False   \n",
       "15               False       False     False     False        False  False   \n",
       "16               False       False     False     False        False  False   \n",
       "17               False       False     False     False        False  False   \n",
       "18               False       False     False     False        False  False   \n",
       "19               False       False     False     False        False  False   \n",
       "20               False       False     False     False        False  False   \n",
       "21               False       False     False     False        False  False   \n",
       "22               False       False     False     False        False  False   \n",
       "23               False       False     False     False        False  False   \n",
       "24               False       False     False     False        False  False   \n",
       "25               False       False     False     False        False  False   \n",
       "26               False       False     False     False        False  False   \n",
       "27               False       False     False     False        False  False   \n",
       "28               False       False     False     False        False  False   \n",
       "29               False       False     False     False        False  False   \n",
       "..                 ...         ...       ...       ...          ...    ...   \n",
       "171              False       False     False     False        False  False   \n",
       "172              False       False     False     False        False  False   \n",
       "173              False       False     False     False        False  False   \n",
       "174              False       False     False     False        False  False   \n",
       "175              False       False     False     False        False  False   \n",
       "176              False       False     False     False        False  False   \n",
       "177              False       False     False     False        False  False   \n",
       "178              False       False     False     False        False  False   \n",
       "179              False       False     False     False        False  False   \n",
       "180              False       False     False     False        False  False   \n",
       "181              False       False     False     False        False  False   \n",
       "182              False       False     False     False        False  False   \n",
       "183              False       False     False     False        False  False   \n",
       "184              False       False     False     False        False  False   \n",
       "185              False       False     False     False        False  False   \n",
       "186              False       False     False     False        False  False   \n",
       "187              False       False     False     False        False  False   \n",
       "188              False       False     False     False        False  False   \n",
       "189              False       False     False     False        False  False   \n",
       "190              False       False     False     False        False  False   \n",
       "191              False       False     False     False        False  False   \n",
       "192              False       False     False     False        False  False   \n",
       "193              False       False     False     False        False  False   \n",
       "194              False       False     False     False        False  False   \n",
       "195              False       False     False     False        False  False   \n",
       "196              False       False     False     False        False  False   \n",
       "197              False       False     False     False        False  False   \n",
       "198              False       False     False     False        False  False   \n",
       "199              False       False     False     False        False  False   \n",
       "200              False       False     False     False        False  False   \n",
       "\n",
       "     city-L/100km  horsepower-binned  diesel    gas  \n",
       "0           False              False   False  False  \n",
       "1           False              False   False  False  \n",
       "2           False              False   False  False  \n",
       "3           False              False   False  False  \n",
       "4           False              False   False  False  \n",
       "5           False              False   False  False  \n",
       "6           False              False   False  False  \n",
       "7           False              False   False  False  \n",
       "8           False              False   False  False  \n",
       "9           False              False   False  False  \n",
       "10          False              False   False  False  \n",
       "11          False              False   False  False  \n",
       "12          False              False   False  False  \n",
       "13          False              False   False  False  \n",
       "14          False              False   False  False  \n",
       "15          False              False   False  False  \n",
       "16          False              False   False  False  \n",
       "17          False              False   False  False  \n",
       "18          False              False   False  False  \n",
       "19          False              False   False  False  \n",
       "20          False              False   False  False  \n",
       "21          False              False   False  False  \n",
       "22          False              False   False  False  \n",
       "23          False              False   False  False  \n",
       "24          False              False   False  False  \n",
       "25          False              False   False  False  \n",
       "26          False              False   False  False  \n",
       "27          False              False   False  False  \n",
       "28          False              False   False  False  \n",
       "29          False              False   False  False  \n",
       "..            ...                ...     ...    ...  \n",
       "171         False              False   False  False  \n",
       "172         False              False   False  False  \n",
       "173         False              False   False  False  \n",
       "174         False              False   False  False  \n",
       "175         False              False   False  False  \n",
       "176         False              False   False  False  \n",
       "177         False              False   False  False  \n",
       "178         False              False   False  False  \n",
       "179         False              False   False  False  \n",
       "180         False              False   False  False  \n",
       "181         False              False   False  False  \n",
       "182         False              False   False  False  \n",
       "183         False              False   False  False  \n",
       "184         False              False   False  False  \n",
       "185         False              False   False  False  \n",
       "186         False              False   False  False  \n",
       "187         False              False   False  False  \n",
       "188         False              False   False  False  \n",
       "189         False              False   False  False  \n",
       "190         False              False   False  False  \n",
       "191         False              False   False  False  \n",
       "192         False              False   False  False  \n",
       "193         False              False   False  False  \n",
       "194         False              False   False  False  \n",
       "195         False              False   False  False  \n",
       "196         False              False   False  False  \n",
       "197         False              False   False  False  \n",
       "198         False              False   False  False  \n",
       "199         False              False   False  False  \n",
       "200         False              False   False  False  \n",
       "\n",
       "[201 rows x 29 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, analyzing a grid of this style to detect missing value is not very convenient, so we will use heatmaps to visually detect these missing values.\n",
    "\n",
    "### Heatmap\n",
    "\n",
    "Heatmap takes a rectangular data grid as input and then assigns a color intensity to each data cell based on the data value of the cell. This is a great way to get visual clues about the data.\n",
    "\n",
    "We will generate a heatmap of the output of isnull() in order to detect missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f847f8c0cf8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates that “stroke” and “horsepower-binned” columns have few missing values.\n",
    "\n",
    "We can handle missing values in many ways:\n",
    "\n",
    "- **Delete:** You can delete the rows with the missing values or delete the whole column which has missing values. The dropna() method from Pandas library can be used to accomplish this task.\n",
    "- **Impute:** Deleting data might cause huge amount of information loss. So, replacing data might be a better option than deleting. One standard replacement technique is to replace missing values with the average value of the entire column. For example, we can replace the missing values in “stroke” column with the mean value of stroke column. The fillna() method from Pandas library can be used to accomplish this task.\n",
    "- **Predictive filling:** Alternatively, you can choose to fill missing values through predictive filling. The interpolate() method will perform a linear interpolation in order to “guess” the missing values and fill the results in the dataset.\n",
    "\n",
    "## ANOVA (Analysis of Variance)\n",
    "\n",
    "ANOVA is a statistical method which is used for figuring out the relation between different groups of categorical data. The ANOVA test, gives us two measures as result:\n",
    "\n",
    "F-test score: It calculates the variation between sample group means divided by variation within sample group.\n",
    "\n",
    "P value: It shows us the confidence degree. In other words, it tells us whether the obtained result is statistically significant or not.\n",
    "\n",
    "Let’s take an example to understand this better.\n",
    "\n",
    "The following bar chart shows the average price of different car makes.\n",
    "\n",
    "<img src=\"http://www.codeheroku.com/static/blog/images/pid17_anova1.png\">\n",
    "\n",
    "We can see that the average price of “audi” and “volvo” is almost same. But, the average price of “jaguar” and “honda” differ significantly.\n",
    "\n",
    "So, we can say that there is very small variance between “audi” and “volvo” because their average price is almost same. While the variance between “jaguar” and “honda” is significantly high. Let’s verify this using the ANOVA method.\n",
    "\n",
    "The ANOVA test can be performed using the f_oneway() method from Scipy library ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=0.014303241552631388, pvalue=0.9063901597143602)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = df[['make','price']].groupby(['make'])\n",
    "\n",
    "stats.f_oneway(temp_df.get_group('audi')['price'],temp_df.get_group('volvo')['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result confirms what we guessed at first. Since the variance between the price of “audi” and “volvo” is very small, we got a F-test score which is very small (around 0.01) and a p value around 0.9.\n",
    "\n",
    "Let’s do this test once more between “jaguar” and “honda” and see the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=400.925870564337, pvalue=1.0586193512077862e-11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(temp_df.get_group('jaguar')['price'],temp_df.get_group('honda')['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that in this case, we got a very high F-Test score(around 401) with a p value around 1.05 * 10^-11 because, the variance between the average price of “jaguar” and “honda” is huge.\n",
    "\n",
    "## Correlation\n",
    "\n",
    "Correlation is a statistical metric for measuring to what extent different variables are interdependent.\n",
    "\n",
    "In other words, when we look at two variables over time, if one variable changes, how does\n",
    "\n",
    "this effect change in the other variable?\n",
    "\n",
    "For example, smoking is known to be correlated with lung cancer. Since, smoking increases the chances of lung cancer.\n",
    "\n",
    "Another example would be the relationship between the number of hours a student studies and the score obtained by that student. Because, we expect the student who studies more to obtain higher marks in the exam.\n",
    "\n",
    "We can see the correlation between different variables using the corr() function. Then we can plot a heatmap over this output to visualize the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f847f99c240>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,16)) #Plot a big(16x16) figure\n",
    "\n",
    "correlation_matrix = df.corr()\n",
    "\n",
    "sns.heatmap(correlation_matrix, annot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above heatmap, we can see that engine size and price are positively correlated(score of 0.87) with each other while, highway-mpg and price are negatively correlated(score of -0.7) with each other. In other words, it tells us that cars with larger engine sizes will be costlier than cars with small engine sizes. It also tells us that expensive cars generally have less MPG as compared to cheaper cars.\n",
    "\n",
    "Let’s verify this relationship by plotting regression plots between these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f847ef994e0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='engine-size',y='price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the positive correlation between engine size and price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f847ef46be0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='highway-mpg',y='price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows us the negative correlation between “highway-mpg” and “price”.\n",
    "\n",
    "This was a brief introduction to Exploratory Data Analysis. Follow our [Medium publication](https://medium.com/code-heroku) to get regular updates on these kind of tutorials.\n",
    "\n",
    "If you enjoyed reading this article, please have a look at our [Introduction to Machine Learning](http://www.codeheroku.com/course?course_id=1) course at [Code Heroku](http://www.codeheroku.com/).\n",
    "\n",
    "<br><br>\n",
    "<p align=\"center\">\n",
    "<a href=\"http://www.codeheroku.com/\">\n",
    "<img src=\"http://www.codeheroku.com/static/images/logo5.png\">\n",
    "</a>\n",
    "</p>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
